docs: def remez_fir(data, freqmin, freqmax, df):
      """
      The minimax optimal bandpass using Remez algorithm. (experimental)
  
      .. warning:: This is experimental code. Use with caution!
  
      :type data: numpy.ndarray
      :param data: Data to filter.
      :param freqmin: Low corner frequency.
      :param freqmax: High corner frequency.
      :param df: Sampling rate in Hz.
      :return: Filtered data.
  
      Finite impulse response (FIR) filter whose transfer function minimizes
      the maximum error between the desired gain and the realized gain in the
      specified bands using the Remez exchange algorithm.
  
      .. versionadded:: 0.6.2
      """
      # Remez filter description
      # ========================
      #
      # So, let's go over the inputs that you'll have to worry about.
      # First is numtaps. This parameter will basically determine how good your
      # filter is and how much processor power it takes up. If you go for some
      # obscene number of taps (in the thousands) there's other things to worry
      # about, but with sane numbers (probably below 30-50 in your case) that is
      # pretty much what it affects (more taps is better, but more expensive
      #         processing wise). There are other ways to do filters as well
      # which require less CPU power if you really need it, but I doubt that you
      # will. Filtering signals basically breaks down to convolution, and apple
      # has DSP libraries to do lightning fast convolution I'm sure, so don't
      # worry about this too much. Numtaps is basically equivalent to the number
      # of terms in the convolution, so a power of 2 is a good idea, 32 is
      # probably fine.
      #
      # bands has literally your list of bands, so you'll break it up into your
      # low band, your pass band, and your high band. Something like [0, 99, 100,
      # 999, 1000, 22049] should work, if you want to pass frequencies between
      # 100-999 Hz (assuming you are sampling at 44.1 kHz).
      #
      # desired will just be [0, 1, 0] as you want to drop the high and low
      # bands, and keep the middle one without modifying the amplitude.
      #
      # Also, specify Hz = 44100 (or whatever).
      #
      # That should be all you need; run the function and it will spit out a list
      # of coefficients [c0, ... c(N-1)] where N is your tap count. When you run
      # this filter, your output signal y[t] will be computed from the input x[t]
      # like this (t-N means N samples before the current one):
      #
      # y[t] = c0*x[t] + c1*x[t-1] + ... + c(N-1)*x[t-(N-1)]
      #
      # After playing around with remez for a bit, it looks like numtaps should
      # be above 100 for a solid filter. See what works out for you. Eventually,
      # take those coefficients and then move them over and do the convolution
      # in C or whatever. Also, note the gaps between the bands in the call to
      # remez. You have to leave some space for the transition in frequency
      # response to occur, otherwise the call to remez will complain.
      #
      # Source:
      # http://episteme.arstechnica.com/
      #         eve/forums/a/tpc/f/6330927813/m/175006289731
      #
      # take 10% of freqmin and freqmax as """corners"""
      flt = freqmin - 0.1 * freqmin
      fut = freqmax + 0.1 * freqmax
      # bandpass between freqmin and freqmax
      filt = remez(50, np.array([0, flt, freqmin, freqmax, fut, df / 2 - 1]),
                   np.array([0, 1, 0]), Hz=df)
      return convolve(filt, data)